Individual fairness is a fairness criterion in which two similar individuals should have the same classification [1]}. It was presented without an algorithmic way to compute similarity and only as means to evaluate a set of given labels. A follow-up work assumes access to an oracle which can evaluate the distance between two individuals according to an unknown but fixed fair metric and show how to learn a fair classifier with a limited number of oracle queries [2]}. In our work we consider \(K\)  neighbors and not the nearest one, and learn the appropriate metric by employing disentanglement. Equally important, our notion of locality, which considers the nearest neighbors out of the closest samples with a the same label is different, and so is our loss, which is based on the distribution of the protected attribute in the obtained set of neighbors.
